@Crypto_hedyEth: Most people waste a lot of time searching for quality AI resources. This GitHub repo quietly released 13 free AI books. All substance, no fluff. https://github.com/AniruddhaChattopadhyay/Books… What's inside: LLM basics → Tokenization…
Summary
This GitHub repo provides 13 free AI/ML books, covering LLM, reinforcement learning, deep learning interviews, and more.
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Cached at: 06/02/26, 05:57 AM
Most people waste a huge amount of time hunting for quality AI resources.
This GitHub repo quietly released 13 free AI books. All packed with substance—no fluff.
https://github.com/AniruddhaChattopadhyay/Books…
What’s inside
LLM Foundations
→ Tokenization to safety
→ Training explained simply
→ Deep dives for newcomers
Reinforcement Learning
→ Value-based methods
→ Policy gradient methods
→ Practical implementation tips
Deep Learning Interviews
→ 400+ curated Q&As
→ From CNNs to Transformers
→ Perfect for last-minute revision
Math for Machine Learning
→ Linear algebra essentials
→ Calculus & probability
→ Real-world examples included
OpenAI Agent Guide
→ Proven design patterns
→ Agent orchestration tips
→ Guardrails for real-world use
Pen & Paper Machine Learning
→ Theory-first problems
→ Step-by-step solutions
→ No keyboard required
Fine-Tuning LLMs
→ From basics to breakthroughs
→ Best practices explained
→ Applied research challenges
Multi-Agent Reinforcement Learning
→ Game theory × learning
→ Core concepts
→ Cutting-edge research
ML Systems Engineering
→ Latest Harvard guide
→ Distributed training
→ AGI-scale systems
Save for later
AniruddhaChattopadhyay/Books
Source: https://github.com/AniruddhaChattopadhyay/Books
📚 AI / ML Bookshelf
Welcome to my personal reference shelf of freely shareable AI & Machine-Learning books.
I keep the PDFs here so I can grep formulas, revisit algorithms, and point friends straight to the good stuff.
Table of contents
| # | Title | Snapshot |
|---|---|---|
| 1 | Deep Learning Interviews | 400+ curated Q&As spanning CNNs, transformers, maths and system design—perfect for pre-interview rapid-fire revision. |
| 2 | Foundation of LLM.pdf | A newcomer-friendly primer on how large language models are built, trained and aligned, from tokenization to safety. |
| 3 | Reinforcement Learning – An Overview | A panoramic survey of modern RL: value-based, policy-gradient, model-based and hybrid methods, with practical tips and further reading. |
| 4 | Alg4ai.pdf | Concise Stanford-style notes covering search, constraint satisfaction, probabilistic reasoning and planning in ~150 pages. |
| 5 | Math4ml.pdf | Linear algebra, calculus and probability essentials explained for ML practitioners, loaded with intuitive worked examples. |
| 6 | OpenAI guide to building practical agents | Design patterns, orchestration tricks and guardrails for shipping real-world AI agents with the OpenAI tool-chain. |
| 7 | Pen and paper exercise in ML | A workbook of theory-first problems (with solutions) to deepen mathematical intuition—no keyboard required. |
| 8 | Matrixcookbook | A concise “cheat-sheet” of hundreds of matrix identities, derivatives, decompositions, and statistical formulas you’ll reach for whenever linear-algebra algebra gets hairy; perfect as a desktop reference to speed up proofs and ML math. |
| 9 | Finetuning guide | The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities. |
| 10 | MULTI-AGENT REINFORCEMENT LEARNING | A definitive introduction to multi-agent reinforcement learning, this book blends game theory and deep learning to offer both foundational insights and cutting-edge research—ideal for newcomers and experts alike. |
| 11 | Context Engineering | A comprehensive 150+ pages survey on context engineering |
| 12 | Linear Algebra Essence and form book | A linear algebra book that connects to concepts in AI |
| 13 | Machine Learning Systems | A comprehensive, up-to-date guide from Harvard on ML Systems Engineering — covering everything from deep learning foundations to distributed training, model optimization, and emerging AGI-scale systems. |
How to use
- Clone the repo
git clone https://github.com/AniruddhaChattopadhyay/Books.git - Open any PDF in your favourite reader—or preview directly on GitHub.
- Search the folder (ripgrep, Spotlight, etc.) when you half-remember that derivation.
- ⭐ Star the repo to catch new additions whenever I find a gem.
Contributing
Have a legally distributable AI/ML book that belongs here? Open a PR with the PDF and add a two-line description to this table. No pay-walled or pirated material, please.
License & attribution
Each PDF retains its original license (usually CC-BY-NC or similar)—see inside the book for details. This README and folder structure are released under the MIT License.
All materials are publicly available under the authors’ distribution terms. If a publisher requests removal, I will comply immediately. Support the authors—buy the print editions or leave reviews if you find these texts valuable.
Happy reading & building! 🚀
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